@InProceedings{FelgueirasCamaOrti:2015:AbGeIn,
               author = "Felgueiras, Carlos Alberto and Camargo, Eduardo Celso Gerbi and 
                         Ortiz, Jussara de Oliveira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Abordagem geoestat{\'{\i}}stica por indica{\c{c}}{\~a}o com 
                         uso de copulas bivariadas emp{\'{\i}}ricas para modelagem de 
                         incertezas associadas a imagens de sensoriamento remoto",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6373--6380",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This paper presents an indicator geoestatistical methodology based 
                         on empirical bivariate copulas for spatial uncertainty modeling 
                         for remote sensing images. As the size of remote sensing images is 
                         usually very large it is used a random sample set sufficient to 
                         represent the spatial variability of the entire image. The sample 
                         set is considered as input to establish the structure of the 
                         spatial correlation via indicator semivariograms using empirical 
                         bivariate copulas. A set of cutoff values is considered to obtain 
                         the indicator semivariograms. The indicator semivariograms are 
                         fitted by mathematical models in order to be used as input, along 
                         with the samples, for indicator geostatistical approaches of 
                         kriging estimations. A case study is presented with China-Brazil 
                         Earth Remote Satellite (CBERS) images from the Amazon forest 
                         region considering deforested and no deforested areas. The results 
                         of the case study are reported along with spatial analyses 
                         considering aspects of the uncertainty related to the 
                         representations and estimations.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1384",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4HQA",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4HQA",
           targetfile = "p1384.pdf",
                 type = "Modelagem espacial",
        urlaccessdate = "27 abr. 2024"
}